Intelligent Alzheimer's Disease Diagnosing Using a Deep Learning Model
Received: 13 April 2025 | Revised: 14 May 2025 and 26 May 2025 | Accepted: 1 June 2025 | Online: 2 August 2025
Corresponding author: Dilena M. Bajalan
Abstract
Alzheimer's Disease (AD) is a significant global health concern. As a progressive neurological disorder, AD, a common cause of dementia, leads to a gradual decline in the cognitive function and the ability to perform daily activities. Since early intervention is crucial for helping patients maintain a higher quality of life, researchers are increasingly turning to new technologies for early detection. Artificial Intelligence (AI) and Deep Learning (DL) are proving to be powerful allies in the effort to identify AD sooner, due to their ability to analyze complex medical data, like Magnetic Resonance Imaging (MRI) scans, uncovering patterns that may be missed by the human eye. To contribute to this growing field, this paper proposes two distinct DL models that leverage the brain MRI data. The first approach utilizes a Convolutional Neural Network (CNN) for a binary classification task to distinguish between the healthy individuals and those with dementia. Building on this, the second model offers a more detailed, four-tiered classification system to identify the specific stage of the disease: Non-Demented (ND), Very Mild Demented (VMD), Mild Demented (MD), and Moderate Demented (MOD).
Keywords:
Alzheimer’s disease, convolutional neural network, deep learning, VGG16 model, EfficientNetB0Downloads
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